Harmful epistatic (genetic) interactions not only occur between mutations, but also when genes change in expression. Gene expression dynamics in yeast suggests that this ‘epigenetic' epistasis constrains evolution, with the tight regulation of network hubs promoting a robust, ‘canalized' phenotype.

Yeast genes with many negative genetic interaction partners tend to have expression that is stable between cells, across conditions, and through evolution.This low expression variation is linked to the use of alternative promoter architectures.The stable expression of genetic interaction network hubs suggests that epigenetic epistasis confers a constraint on evolution.

Reduced activity of two genes in combination often has a more detrimental effect than expected. Such epistatic interactions not only occur when genes are mutated but also due to variation in gene expression, including among isogenic individuals in a controlled environment. We hypothesized that these ‘epigenetic' epistatic interactions could place important constraints on the evolution of gene expression. Consistent with this, we show here that yeast genes with many epistatic interaction partners typically show low expression variation among isogenic individuals and low variation across different conditions. In addition, their expression tends to remain stable in response to the accumulation of mutations and only diverges slowly between strains and species. Yeast promoter architectures, the retention of gene duplicates, and the divergence of expression between humans and chimps are also consistent with selective pressure to reduce the likelihood of harmful epigenetic epistatic interactions. Based on these and previous analyses, we propose that the tight regulation of epistatic interaction network hubs makes an important contribution to the maintenance of a robust, ‘canalized' phenotype. Moreover, that epigenetic epistatic interactions may contribute substantially to fitness defects when single genes are deleted.

PySB is a framework for creating biological models as Python programs using a high-level, action-oriented vocabulary that promotes transparency, extensibility and reusability. PySB interoperates with many existing modeling tools and supports distributed model development.

PySB models are programs and leverage existing programming tools for documentation, testing, and collaborative development.Reusable functions can encode common low-level biochemical processes as well as high-level modules, making models transparent and concise.Modeling workflow is accelerated through close integration with Python numerical tools and interoperability with existing modeling software.We demonstrate the use of PySB to encode 15 alternative hypotheses for the mitochondrial regulation of apoptosis, including a new ‘Embedded Together' model based on recent biochemical findings.

Mathematical equations are fundamental to modeling biological networks, but as networks get large and revisions frequent, it becomes difficult to manage equations directly or to combine previously developed models. Multiple simultaneous efforts to create graphical standards, rule-based languages, and integrated software workbenches aim to simplify biological modeling but none fully meets the need for transparent, extensible, and reusable models. In this paper we describe PySB, an approach in which models are not only created using programs, they are programs. PySB draws on programmatic modeling concepts from little b and ProMot, the rule-based languages BioNetGen and Kappa and the growing library of Python numerical tools. Central to PySB is a library of macros encoding familiar biochemical actions such as binding, catalysis, and polymerization, making it possible to use a high-level, action-oriented vocabulary to construct detailed models. As Python programs, PySB models leverage tools and practices from the open-source software community, substantially advancing our ability to distribute and manage the work of testing biochemical hypotheses. We illustrate these ideas using new and previously published models of apoptosis.

Escherichia coli cells were evolved over 500 generations and profiled in four abiotic stressors to observe several cases of emerging cross-stress behavior whereby adaptation to one stressful environment provided fitness advantage when exposed to a second stressor.

Cross-stress dependencies were found to be ubiquitous, highly interconnected and can emerge within short timeframes.Several targets were implicated in adaptation and cross-stress protection, including genes related to iron transport and flagella.Adaptation in a first stress can lead to higher fitness to a second stress when compared with cells adapted only in the latter environment.Adaptation to any specific stress and the growth media was found to be generally independent.

Bacterial populations have a remarkable capacity to cope with extreme environmental fluctuations in their natural environments. In certain cases, adaptation to one stressful environment provides a fitness advantage when cells are exposed to a second stressor, a phenomenon that has been coined as cross-stress protection. A tantalizing question in bacterial physiology is how the cross-stress behavior emerges during evolutionary adaptation and what the genetic basis of acquired stress resistance is. To address these questions, we evolved Escherichia coli cells over 500 generations in five environments that include four abiotic stressors. Through growth profiling and competition assays, we identified several cases of positive and negative cross-stress behavior that span all strain–stress combinations. Resequencing the genomes of the evolved strains resulted in the identification of several mutations and gene amplifications, whose fitness effect was further assessed by mutation reversal and competition assays. Transcriptional profiling of all strains under a specific stress, NaCl-induced osmotic stress, and integration with resequencing data further elucidated the regulatory responses and genes that are involved in this phenomenon. Our results suggest that cross-stress dependencies are ubiquitous, highly interconnected, and can emerge within short timeframes. The high adaptive potential that we observed argues that bacterial populations occupy a genotypic space that enables a high phenotypic plasticity during adaptation in fluctuating environments.

A Bayesian framework is used to calibrate a mass-action model of receptor-mediated apoptosis. Despite parameter non-identifiability and model ‘sloppiness', Bayes factor analysis discriminates between two alternative models of mitochondrial outer membrane permeabilization.

Bayesian estimation returns statistically complete joint parameter distribution for mass-action models of receptor-mediated apoptosis calibrated to dynamic, live-cell data.Analysis of joint distributions reveals strong, non-linear correlations between parameters that are poorly captured by a conventional table of mean values and covariances; a high-dimensional distribution must therefore be reported as the true estimate of parameter values.Despite non-identifiablility and model ‘sloppiness,' a Bayesian framework returns probabilistic predictions for cell death dynamics that have tight confidence intervals and match experimental data.Use of a Bayesian framework to discriminate between two competing models of mitochondrial outer membrane permeabilization shows that a ‘direct' mechanism has ∼20-fold greater plausibility than an ‘indirect' mechanism, even though both models exhibit equally good fits to data for some parameters.

Using models to simulate and analyze biological networks requires principled approaches to parameter estimation and model discrimination. We use Bayesian and Monte Carlo methods to recover the full probability distributions of free parameters (initial protein concentrations and rate constants) for mass-action models of receptor-mediated cell death. The width of the individual parameter distributions is largely determined by non-identifiability but covariation among parameters, even those that are poorly determined, encodes essential information. Knowledge of joint parameter distributions makes it possible to compute the uncertainty of model-based predictions whereas ignoring it (e.g., by treating parameters as a simple list of values and variances) yields nonsensical predictions. Computing the Bayes factor from joint distributions yields the odds ratio (∼20-fold) for competing ‘direct' and ‘indirect' apoptosis models having different numbers of parameters. Our results illustrate how Bayesian approaches to model calibration and discrimination combined with single-cell data represent a generally useful and rigorous approach to discriminate between competing hypotheses in the face of parametric and topological uncertainty.

Human brain transcriptome analysis revealed widespread age-related splicing changes in the prefrontal cortex and cerebellum. While most of the splicing changes take place in development, approximately one-third of them extends into aging.

More than one-third of genes expressed in the human brain change splicing with age.Approximately 30% of observed splicing changes occur in aging.Age-related splicing patterns are largely conserved between the human and macaque brains.High frequency of intron retention events suggests the role of nonsense-mediated decay in age-related gene expression regulation.

While splicing differences between tissues, sexes and species are well documented, little is known about the extent and the nature of splicing changes that take place during human or mammalian development and aging. Here, using high-throughput transcriptome sequencing, we have characterized splicing changes that take place during whole human lifespan in two brain regions: prefrontal cortex and cerebellum. Identified changes were confirmed using independent human and rhesus macaque RNA-seq data sets, exon arrays and PCR, and were detected at the protein level using mass spectrometry. Splicing changes across lifespan were abundant in both of the brain regions studied, affecting more than a third of the genes expressed in the human brain. Approximately 15% of these changes differed between the two brain regions. Across lifespan, splicing changes followed discrete patterns that could be linked to neural functions, and associated with the expression profiles of the corresponding splicing factors. More than 60% of all splicing changes represented a single splicing pattern reflecting preferential inclusion of gene segments potentially targeting transcripts for nonsense-mediated decay in infants and elderly.

Precise analysis of systematic errors shows suitability of immunofluorescence protocols to quantify gene expression means, variances, and cross-correlations. Application to Drosophila gap genes enables reconstructing expression level dynamics and the progression of positional accuracy.

A careful analysis of the contribution of multiple sources of measurement errors shows that <20% of the observed embryo-to-embryo fluctuations stem from experimental error.Intensities and slopes of the borders of gap gene expression patterns simultaneously reach a maximum around 15 min before gastrulation in a precisely coordinated fashion, hinting at an intrinsically collective organization of the gap gene network.The reproducibility of gap gene expression levels increases two-fold before reaching a maximum when the overall network dynamics peak. At the same time, the positional accuracy of determining cell fates is half an internuclear distance and uniform along the entire embryo length.

Quantification of gene expression has become a central tool for understanding genetic networks. In many systems, the only viable way to measure protein levels is by immunofluorescence, which is notorious for its limited accuracy. Using the early Drosophila embryo as an example, we show that careful identification and control of experimental error allows for highly accurate gene expression measurements. We generated antibodies in different host species, allowing for simultaneous staining of four Drosophila gap genes in individual embryos. Careful error analysis of hundreds of expression profiles reveals that less than ∼20% of the observed embryo-to-embryo fluctuations stem from experimental error. These measurements make it possible to extract not only very accurate mean gene expression profiles but also their naturally occurring fluctuations of biological origin and corresponding cross-correlations. We use this analysis to extract gap gene profile dynamics with ∼1 min accuracy. The combination of these new measurements and analysis techniques reveals a twofold increase in profile reproducibility owing to a collective network dynamics that relays positional accuracy from the maternal gradients to the pair-rule genes.

Escherichia coli were engineered to enable programmed motility, sensing and phenotypic response to the density of epidermal growth factor receptor expressed on the surface of cancer cells.

Bacteria were engineered to display targeted motility through AI-2-mediated chemotaxis.Recruitment of motile bacteria was achieved by site-specific synthesis of quorum sensing autoinducers using anti-EGFR nanofactories.Threshold-based switching of bacterial gene expression was controlled by AI-2 quorum sensing.The engineered ‘bacterial dirigible' represents a new means for targeted drug delivery and may have multiple applications wherein bacterial cells are designed to carry out specified tasks.

Escherichia coli were genetically modified to enable programmed motility, sensing, and actuation based on the density of features on nearby surfaces. Then, based on calculated feature density, these cells expressed marker proteins to indicate phenotypic response. Specifically, site-specific synthesis of bacterial quorum sensing autoinducer-2 (AI-2) is used to initiate and recruit motile cells. In our model system, we rewired E. coli's AI-2 signaling pathway to direct bacteria to a squamous cancer cell line of head and neck (SCCHN), where they initiate synthesis of a reporter (drug surrogate) based on a threshold density of epidermal growth factor receptor (EGFR). This represents a new type of controller for targeted drug delivery as actuation (synthesis and delivery) depends on a receptor density marking the diseased cell. The ability to survey local surfaces and initiate gene expression based on feature density represents a new area-based switch in synthetic biology that will find use beyond the proposed cancer model here.

Phosphorylation sites of human proteins are frequently mutated in cancer. Statistical analysis of phosphorylation-associated single nucleotide variants (pSNVs) predicts novel cancer drivers and phospho-mutation mechanisms in known cancer genes.

We designed the ActiveDriver method to identify significantly mutated signaling regions in proteins. ActiveDriver is complementary to standard frequency-based methods of mutation significance and helps interpret rare, but site-specific mutations.Analysis of somatic mutations in 800 cancer genomes reveals dozens of known and novel cancer genes, including potential drivers that are apparent only when integrating multiple cancer types.Pathway and network analysis identifies systems with significantly enriched pSNVs, including kinase modules and protein complexes.Clinical data analysis identifies phospho-mutations of TP53 that correlate with prolonged patient survival in ovarian and brain cancer. Kinase network analysis highlights multiple survival-associated signaling modules with pSNVs.

Large-scale cancer genome sequencing has uncovered thousands of gene mutations, but distinguishing tumor driver genes from functionally neutral passenger mutations is a major challenge. We analyzed 800 cancer genomes of eight types to find single-nucleotide variants (SNVs) that precisely target phosphorylation machinery, important in cancer development and drug targeting. Assuming that cancer-related biological systems involve unexpectedly frequent mutations, we used novel algorithms to identify genes with significant phosphorylation-associated SNVs (pSNVs), phospho-mutated pathways, kinase networks, drug targets, and clinically correlated signaling modules. We highlight increased survival of patients with TP53 pSNVs, hierarchically organized cancer kinase modules, a novel pSNV in EGFR, and an immune-related network of pSNVs that correlates with prolonged survival in ovarian cancer. Our findings include multiple actionable cancer gene candidates (FLNB, GRM1, POU2F1), protein complexes (HCF1, ASF1), and kinases (PRKCZ). This study demonstrates new ways of interpreting cancer genomes and presents new leads for cancer research.

Genome sequencing technologies have advanced rapidly, dramatically decreasing cost and increasing throughput. But beyond faster and cheaper, these advances have also stimulated the development of innovative new experimental approaches, and are opening new doors in human medicine and health.

Advances in genome sequencing have progressed at a rapid pace, with increased throughput accompanied by plunging costs. But these advances go far beyond faster and cheaper. High-throughput sequencing technologies are now routinely being applied to a wide range of important topics in biology and medicine, often allowing researchers to address important biological questions that were not possible before. In this review, we discuss these innovative new approaches—including ever finer analyses of transcriptome dynamics, genome structure and genomic variation—and provide an overview of the new insights into complex biological systems catalyzed by these technologies. We also assess the impact of genotyping, genome sequencing and personal omics profiling on medical applications, including diagnosis and disease monitoring. Finally, we review recent developments in single-cell sequencing, and conclude with a discussion of possible future advances and obstacles for sequencing in biology and health.

A simple, parameterless mathematical model, in combination with real-time monitoring of promoter activities, shows how control of gene expression in bacteria is shared between transcription factors and global physiological effects.

We present an approach based on a simple, paramaterless mathematical model to analyze the control of gene expression by transcription factors and the global physiological state of the cell.We illustrate the strength of this approach by means of time-resolved measurements of the transcriptional activities of genes in a central regulatory circuit in Escherichia coli.We conclude that global physiological effects rather than transcription factors dominate the control of gene expression during a growth transition.Our results call for a reappraisal of the role of transcription factors, which may be most appropriately viewed as complementing and finetuning global control exerted by the physiological state of the cell.

Gene expression is controlled by the joint effect of (i) the global physiological state of the cell, in particular the activity of the gene expression machinery, and (ii) DNA-binding transcription factors and other specific regulators. We present a model-based approach to distinguish between these two effects using time-resolved measurements of promoter activities. We demonstrate the strength of the approach by analyzing a circuit involved in the regulation of carbon metabolism in E. coli. Our results show that the transcriptional response of the network is controlled by the physiological state of the cell and the signaling metabolite cyclic AMP (cAMP). The absence of a strong regulatory effect of transcription factors suggests that they are not the main coordinators of gene expression changes during growth transitions, but rather that they complement the effect of global physiological control mechanisms. This change of perspective has important consequences for the interpretation of transcriptome data and the design of biological networks in biotechnology and synthetic biology.

By comparative analysis of RNA polymerase II and FOXO3 ChIP-sequencing, combined with 4C-sequencing and ChIPs on histone modifications, general mechanisms of FOXO3-mediated target gene regulation are identified.

FOXO3 acts as a transcriptional activator, inducing target gene expression through RNA polymerase II recruitment.FOXO3 binds and activates a pre-existing network of distal enhancers.FOXO3 bound distant regulatory regions contribute to target gene regulation.Chromatin architecture could determine the cell type-specific effects of FOXO3 target gene regulation.

Forkhead box O (FOXO) transcription factors are key players in diverse cellular processes affecting tumorigenesis, stem cell maintenance and lifespan. To gain insight into the mechanisms of FOXO-regulated target gene expression, we studied genome-wide effects of FOXO3 activation. Profiling RNA polymerase II changes shows that FOXO3 regulates gene expression through transcription initiation. Correlative analysis of FOXO3 and RNA polymerase II ChIP-seq profiles demonstrates FOXO3 to act as a transcriptional activator. Furthermore, this analysis reveals a significant part of FOXO3 gene regulation proceeds through enhancer regions. FOXO3 binds to pre-existing enhancers and further activates these enhancers as shown by changes in histone acetylation and RNA polymerase II recruitment. In addition, FOXO3-mediated enhancer activation correlates with regulation of adjacent genes and pre-existence of chromatin loops between FOXO3 bound enhancers and target genes. Combined, our data elucidate how FOXOs regulate gene transcription and provide insight into mechanisms by which FOXOs can induce different gene expression programs depending on chromatin architecture.

This review provides an overview of methodologies and technologies enabling genome-scale engineering, focusing on the design, construction, and testing of modified genomes in a variety of organisms. Future applications for systems and synthetic biology are discussed.

Genome-modification technologies enable the rational engineering and perturbation of biological systems. Historically, these methods have been limited to gene insertions or mutations at random or at a few pre-defined locations across the genome. The handful of methods capable of targeted gene editing suffered from low efficiencies, significant labor costs, or both. Recent advances have dramatically expanded our ability to engineer cells in a directed and combinatorial manner. Here, we review current technologies and methodologies for genome-scale engineering, discuss the prospects for extending efficient genome modification to new hosts, and explore the implications of continued advances toward the development of flexibly programmable chasses, novel biochemistries, and safer organismal and ecological engineering.

A new quantitative strategy has generated a comprehensive rate control map for protein synthesis in exponentially growing yeast cells. This analysis reveals the modularity of the system as well as highly non-stoichiometric relationships between components.

A ‘genetic titration' method has generated a map of the in vivo rate control properties of components of the protein synthesis machinery in Saccharomyces cerevisiae and has been used to parameterize a new comprehensive model of the translation pathway.The translation machinery is found to be a highly modular system in functional terms yet the intracellular concentrations of its components range from a few thousand to one million molecules per cell.This approach identifies non-intuitive features of the system such as the strongest rate control being exercised by high abundance elongation factors.The rate control analysis allows us to identify a surprising fine-control function for duplicated translation factor genes.

Rate control analysis defines the in vivo control map governing yeast protein synthesis and generates an extensively parameterized digital model of the translation pathway. Among other non-intuitive outcomes, translation demonstrates a high degree of functional modularity and comprises a non-stoichiometric combination of proteins manifesting functional convergence on a shared maximal translation rate. In exponentially growing cells, polypeptide elongation (eEF1A, eEF2, and eEF3) exerts the strongest control. The two other strong control points are recruitment of mRNA and tRNAi to the 40S ribosomal subunit (eIF4F and eIF2) and termination (eRF1; Dbp5). In contrast, factors that are found to promote mRNA scanning efficiency on a longer than-average 5′untranslated region (eIF1, eIF1A, Ded1, eIF2B, eIF3, and eIF5) exceed the levels required for maximal control. This is expected to allow the cell to minimize scanning transition times, particularly for longer 5′UTRs. The analysis reveals these and other collective adaptations of control shared across the factors, as well as features that reflect functional modularity and system robustness. Remarkably, gene duplication is implicated in the fine control of cellular protein synthesis.

Coordination of many biological processes is necessary for mammalian pre-implantation embryo development. The underlying regulatory network was mapped through mathematical modeling, gene-specific knockdowns, and profiling of pooled embryos, single embryos, and single cells.

An integrated Oct4-Sall4-Nanog regulatory network of protein-coding genes and microRNAs governs developmental progression in pre-implantation mouse embryos.While many target genes are common between embryos and embryonic stem cells (ESCs), pluripotency factors regulate the expression of many metabolism- and transport-related genes only in embryos but not in stem cells.The expression of some genes, including the DNA methyltransferase Dnmt3b, correlates strongly with the extent to which an embryo depleted of Oct4, Sall4, or Nanog can develop.In wild-type embryos and ESCs, a coherent feed-forward loop buffers the expression of Dnmt3b against intrinsic fluctuations in the levels of the pluripotency factors.

Landmark events occur in a coordinated manner during pre-implantation development of the mammalian embryo, yet the regulatory network that orchestrates these events remains largely unknown. Here, we present the first systematic investigation of the network in pre-implantation mouse embryos using morpholino-mediated gene knockdowns of key embryonic stem cell (ESC) factors followed by detailed transcriptome analysis of pooled embryos, single embryos, and individual blastomeres. We delineated the regulons of Oct4, Sall4, and Nanog and identified a set of metabolism- and transport-related genes that were controlled by these transcription factors in embryos but not in ESCs. Strikingly, the knockdown embryos arrested at a range of developmental stages. We provided evidence that the DNA methyltransferase Dnmt3b has a role in determining the extent to which a knockdown embryo can develop. We further showed that the feed-forward loop comprising Dnmt3b, the pluripotency factors, and the miR-290-295 cluster exemplifies a network motif that buffers embryos against gene expression noise. Our findings indicate that Oct4, Sall4, and Nanog form a robust and integrated network to govern mammalian pre-implantation development.

Establishment of cell polarity involves sensing of external cues followed by signal amplification. Analysis of Caenorhabditis elegans P-cell polarity in Wnt ligand and receptor mutants is used to separate the contribution of ligands and receptors to the sensing and amplification processes.

By combining quantitative single molecule transcript counting with phenomenological modeling, we studied the effects of ligand and receptor loss on P cells' division in Caenorhabditis elegans.We found that loss of ligands leads to polarity reversals whereas polarity loss is observed in the receptor mutants.These results suggest that ligands affect primarily the sensing process whereas receptors are needed for both sensing and amplification.Our integrated approach is generally applicable to other systems and will facilitate decoupling of the different layers of signal sensing and amplification.

Establishment of cell polarity is crucial for many biological processes including cell migration and asymmetric cell division. The establishment of cell polarity consists of two sequential processes: an external gradient is first sensed and then the resulting signal is amplified and maintained by intracellular signaling networks usually using positive feedback regulation. Generally, these two processes are intertwined and it is challenging to determine which proteins contribute to the sensing or amplification process, particularly in multicellular organisms. Here, we integrated phenomenological modeling with quantitative single-cell measurements to separate the sensing and amplification components of Wnt ligands and receptors during establishment of polarity of the Caenorhabditis elegans P cells. By systematically exploring how P-cell polarity is altered in Wnt ligand and receptor mutants, we inferred that ligands predominantly affect the sensing process, whereas receptors are needed for both sensing and amplification. This integrated approach is generally applicable to other systems and will facilitate decoupling of the different layers of signal sensing and amplification.

RNAi screening and automated image analysis reveal 180 kinases and phosphatases regulating the organization of the Golgi apparatus. Most of these genes also control the expression of specific glycans, pointing to a web of interactions between signaling cascades and glycosylation at the Golgi.

Golgi organization was probed with three markers of different Golgi compartments and quantitative morphological analysis.Knockdowns of ∼20% of all known kinases and phosphatases affected the Golgi globally or in a compartment-specific manner, and were comparable in degree to the depletion of known membrane traffic regulators such as SNAREs.Several cell surface receptors, their cognate ligands and downstream effectors regulate Golgi organization, suggesting a large regulatory network.Most signaling genes affected both Golgi morphology and the expression of specific glycans.

The Golgi apparatus has many important physiological functions, including sorting of secretory cargo and biosynthesis of complex glycans. These functions depend on the intricate and compartmentalized organization of the Golgi apparatus. To investigate the mechanisms that regulate Golgi architecture, we developed a quantitative morphological assay using three different Golgi compartment markers and quantitative image analysis, and performed a kinome- and phosphatome-wide RNAi screen in HeLa cells. Depletion of 159 signaling genes, nearly 20% of genes assayed, induced strong and varied perturbations in Golgi morphology. Using bioinformatics data, a large regulatory network could be constructed. Specific subnetworks are involved in phosphoinositides regulation, acto-myosin dynamics and mitogen activated protein kinase signaling. Most gene depletion also affected Golgi functions, in particular glycan biosynthesis, suggesting that signaling cascades can control glycosylation directly at the Golgi level. Our results provide a genetic overview of the signaling pathways that control the Golgi apparatus in human cells.

An accurate mathematical model of the mammalian circadian clock provides novel insights into the mechanisms that generate 24-h rhythms. A double-negative feedback loop design is proposed for biological clocks whose period needs to be tightly regulated.

A 1–1 stoichiometric balance and tight binding between activators (PER–CRY) and repressors (BMAL1–CLOCK/NPAS2) is required for sustained rhythmicity.Stoichiometry is balanced by an additional negative feedback loop consisting of a stable activator.Our detailed model can explain more experimental data than previous models.Mathematical analysis of a simple model supports our claims.

Circadian (∼24 h) timekeeping is essential for the lives of many organisms. To understand the biochemical mechanisms of this timekeeping, we have developed a detailed mathematical model of the mammalian circadian clock. Our model can accurately predict diverse experimental data including the phenotypes of mutations or knockdown of clock genes as well as the time courses and relative expression of clock transcripts and proteins. Using this model, we show how a universal motif of circadian timekeeping, where repressors tightly bind activators rather than directly binding to DNA, can generate oscillations when activators and repressors are in stoichiometric balance. Furthermore, we find that an additional slow negative feedback loop preserves this stoichiometric balance and maintains timekeeping with a fixed period. The role of this mechanism in generating robust rhythms is validated by analysis of a simple and general model and a previous model of the Drosophila circadian clock. We propose a double-negative feedback loop design for biological clocks whose period needs to be tightly regulated even with large changes in gene dosage.

A combination of genetic trapping, affinity-capture and selected reaction monitoring mass spectrometry is used to characterize the dynamic proteome of pre-60S ribosomal particles after nuclear export. These results identify Bud20 as a novel shuttling factor for pre-60S export.

Co-enrichment of assembly and transport factors with maturing pre-ribosomal particles can be reliably and rapidly measured by selected reaction monitoring mass spectrometry (SRM-MS).Genetic trapping and affinity-capture combined with SRM-MS reveal the dynamic proteome pre-60S particles after nuclear export.We identified Bud20 as a novel shuttling factor that facilitates nuclear export of pre-60S particles.Our workflow is a versatile discovery tool to dissect the assembly and transport pathways of diverse large macromolecular assemblies.

Construction and intracellular targeting of eukaryotic pre-ribosomal particles involve a multitude of diverse transiently associating trans-acting assembly factors, energy-consuming enzymes, and transport factors. The ability to rapidly and reliably measure co-enrichment of multiple factors with maturing pre-ribosomal particles presents a major biochemical bottleneck towards revealing their function and the precise contribution of >50 energy-consuming steps that drive ribosome assembly. Here, we devised a workflow that combines genetic trapping, affinity-capture, and selected reaction monitoring mass spectrometry (SRM-MS), to overcome this deficiency. We exploited this approach to interrogate the dynamic proteome of pre-60S particles after nuclear export. We uncovered assembly factors that travel with pre-60S particles to the cytoplasm, where they are released before initiating translation. Notably, we identified a novel shuttling factor that facilitates nuclear export of pre-60S particles. Capturing and quantitating protein interaction networks of trapped intermediates of macromolecular complexes by our workflow is a reliable discovery tool to unveil dynamic processes that contribute to their in vivo assembly and transport.

Network-based analysis of transcriptome dynamics during activation in two human T-cell subpopulations identifies key regulators, and reveals that PLAU plays a critical role in both human and murine regulatory T-cell function.

We construct a Treg-specific correlation network from a high time-resolution transcriptome of human Tregs versus CD4+ T effector cells measured during their very early activation process.We propose a queen bee-surrounding principle to predict key candidate genes from the simplified undirected correlation network rather than an advanced directed transcription regulatory network. These potential key genes would have not been easily identified by a differential expression analysis.We show that the plasminogen activator urokinase (PLAU) is critical for suppressor function of both human and murine Tregs.We further demonstrate that PLAU is particularly important for memory Tregs and that PLAU mediates Treg suppressor function via STAT5 and ERK signaling pathways.

Human FOXP3+CD25+CD4+ regulatory T cells (Tregs) are essential to the maintenance of immune homeostasis. Several genes are known to be important for murine Tregs, but for human Tregs the genes and underlying molecular networks controlling the suppressor function still largely remain unclear. Here, we describe a strategy to identify the key genes directly from an undirected correlation network which we reconstruct from a very high time-resolution (HTR) transcriptome during the activation of human Tregs/CD4+ T-effector cells. We show that a predicted top-ranked new key gene PLAU (the plasminogen activator urokinase) is important for the suppressor function of both human and murine Tregs. Further analysis unveils that PLAU is particularly important for memory Tregs and that PLAU mediates Treg suppressor function via STAT5 and ERK signaling pathways. Our study demonstrates the potential for identifying novel key genes for complex dynamic biological processes using a network strategy based on HTR data, and reveals a critical role for PLAU in Treg suppressor function.

Network-based analysis of transcriptome dynamics during activation in two human T-cell subpopulations identifies key regulators, and reveals that PLAU plays a critical role in both human and murine regulatory T-cell function.

Altruistic death is shown to confer a population level advantage in engineered E. coli. Cost-benefit trade-offs are analyzed and altruistic death is shown to account for the 'Eagle effect', whereby bacteria appear to grow better in high antibiotics concentrations.

We engineered a synthetic system to program tunable altruistic death in bacteria.Our system demonstrated conditions for a population-level advantage of altruistic death.Cost–benefit trade-off results in emergence of an optimal degree of death that is tunable by rates of public-good synthesis.Altruistic death can cause non-monotonic dose responses in antibiotic treatment.

Programmed death is often associated with a bacterial stress response. This behavior appears paradoxical, as it offers no benefit to the individual. This paradox can be explained if the death is ‘altruistic': the killing of some cells can benefit the survivors through release of ‘public goods'. However, the conditions where bacterial programmed death becomes advantageous have not been unambiguously demonstrated experimentally. Here, we determined such conditions by engineering tunable, stress-induced altruistic death in the bacterium Escherichia coli. Using a mathematical model, we predicted the existence of an optimal programmed death rate that maximizes population growth under stress. We further predicted that altruistic death could generate the ‘Eagle effect', a counter-intuitive phenomenon where bacteria appear to grow better when treated with higher antibiotic concentrations. In support of these modeling insights, we experimentally demonstrated both the optimality in programmed death rate and the Eagle effect using our engineered system. Our findings fill a critical conceptual gap in the analysis of the evolution of bacterial programmed death, and have implications for a design of antibiotic treatment.

Ensemble modelling is used to study the yeast high osmolarity glycerol (HOG) pathway, a prototype for eukaryotic mitogen-activated kinase signalling systems. The best fit model provides new insights into the function of this system, some of which are then experimentally validated.

The main mechanism for osmo-adaptation is a fast and transient non-transcriptional Hog1-mediated activation of glycerol production.The transcriptional response rather serves to maintain an increased steady-state glycerol production with low steady-state Hog1 activity after adaptation.A fast negative feedback of activated Hog1 on the upstream signalling branches serves to stabilise the adaptation response by preventing oscillatory behaviour.Two parallel redundant signalling branches elicit a more robust and swifter adaptation than a single branch alone, at least for low osmotic shock. This notion could be corroborated by dedicated measurements of single-cell volume recovery for the wild-type and single-branch mutants.

The high osmolarity glycerol (HOG) pathway in yeast serves as a prototype signalling system for eukaryotes. We used an unprecedented amount of data to parameterise 192 models capturing different hypotheses about molecular mechanisms underlying osmo-adaptation and selected a best approximating model. This model implied novel mechanisms regulating osmo-adaptation in yeast. The model suggested that (i) the main mechanism for osmo-adaptation is a fast and transient non-transcriptional Hog1-mediated activation of glycerol production, (ii) the transcriptional response serves to maintain an increased steady-state glycerol production with low steady-state Hog1 activity, and (iii) fast negative feedbacks of activated Hog1 on upstream signalling branches serves to stabilise adaptation response. The best approximating model also indicated that homoeostatic adaptive systems with two parallel redundant signalling branches show a more robust and faster response than single-branch systems. We corroborated this notion to a large extent by dedicated measurements of volume recovery in single cells. Our study also demonstrates that systematically testing a model ensemble against data has the potential to achieve a better and unbiased understanding of molecular mechanisms.

A strategy is presented that combines metabolic fluxes with targeted phosphoproteomics measurements to drive testable hypotheses for the functionality of post-translational regulation in S. cerevisiae central metabolism.

Discovery-driven mass spectrometry phosphoproteomics identified 35 differentially phosphorylated enzymes of yeast central metabolism.Phosphoenzymes are predominant in upper glycolysis, around the pyruvate node and in carbohydrate storage pathways.A targeted phosphoproteomics method was developed to quantify total, phospho and non-phosphoprotein directly from crude cell extracts.Correlation of phosphoprotein levels with metabolic fluxes across conditions provided functional evidence for five novel phosphoregulated enzymes.Functional follow-ups demonstrated the inhibitory role of phosphorylation in controlling metabolic fluxes realised by Gpd1, Pda1 and Pfk2.

As a frequent post-translational modification, protein phosphorylation regulates many cellular processes. Although several hundred phosphorylation sites have been mapped to metabolic enzymes in Saccharomyces cerevisiae, functionality was demonstrated for few of them. Here, we describe a novel approach to identify in vivo functionality of enzyme phosphorylation by combining flux analysis with proteomics and phosphoproteomics. Focusing on the network of 204 enzymes that constitute the yeast central carbon and amino-acid metabolism, we combined protein and phosphoprotein levels to identify 35 enzymes that change their degree of phosphorylation during growth under five conditions. Correlations between previously determined intracellular fluxes and phosphoprotein abundances provided first functional evidence for five novel phosphoregulated enzymes in this network, adding to nine known phosphoenzymes. For the pyruvate dehydrogenase complex E1 α subunit Pda1 and the newly identified phosphoregulated glycerol-3-phosphate dehydrogenase Gpd1 and phosphofructose-1-kinase complex β subunit Pfk2, we then validated functionality of specific phosphosites through absolute peptide quantification by targeted mass spectrometry, metabolomics and physiological flux analysis in mutants with genetically removed phosphosites. These results demonstrate the role of phosphorylation in controlling the metabolic flux realised by these three enzymes.